Package: HCPclust 0.1.2
HCPclust: Hierarchical Conformal Prediction for Clustered Data with Missing Responses
Implements hierarchical conformal prediction for clustered data with missing responses. The method uses repeated cluster-level splitting and within-cluster subsampling to accommodate dependence, and inverse-probability weighting to correct distribution shift induced by missingness. Conditional densities are estimated by inverting fitted conditional quantiles (linear quantile regression or quantile regression forests), and p-values are aggregated across resampling and splitting steps using the Cauchy combination test.
Authors:
HCPclust_0.1.2.tar.gz
HCPclust_0.1.2.zip(r-4.7-any)HCPclust_0.1.2.zip(r-4.6-any)HCPclust_0.1.2.zip(r-4.5-any)
HCPclust_0.1.2.tgz(r-4.6-any)HCPclust_0.1.2.tgz(r-4.5-any)
HCPclust_0.1.2.tar.gz(r-4.7-any)HCPclust_0.1.2.tar.gz(r-4.6-any)
HCPclust_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
HCPclust/json (API)
| # Install 'HCPclust' in R: |
| install.packages('HCPclust', repos = c('https://judywangstat.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/judywangstat/hcp/issues
Last updated from:d14edc3a03. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 187 | ||
| source / vignettes | OK | 183 | ||
| linux-release | OK | 162 | ||
| macos-release | OK | 196 | ||
| macos-oldrel | OK | 233 | ||
| windows-devel | OK | 85 | ||
| windows-release | OK | 78 | ||
| windows-oldrel | OK | 89 | ||
| wasm-release | OK | 91 |
Exports:fit_cond_density_quantilefit_missingness_propensitygenerate_clustered_marhcp_conformal_regionhcp_predict_targetsplot_hcp_intervals
Dependencies:data.tableDiceKriginggrfjsonlitelatticelmtestMASSMatrixMatrixModelsquantregquantregForestrandomForestRcppRcppEigensandwichSparseMsurvivalxgboostzoo
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Estimate conditional density pi(y|x) via quantile process + quotient estimator | fit_cond_density_quantile |
| Fit missingness propensity model P(delta=1 | X) from pooled data | fit_missingness_propensity |
| Simulate clustered continuous outcomes with covariate-dependent MAR missingness | generate_clustered_mar |
| HCP conformal prediction region with repeated subsampling and repeated data splitting | hcp_conformal_region |
| HCP prediction wrapper for multiple measurements with optional per-patient Bonferroni | hcp_predict_targets |
| Plot HCP prediction intervals (band vs covariate or intervals by patient) | plot_hcp_intervals |
